In the United States, approximately three million people have type 1 diabetes.[1] To treat their condition, these patients depend either on multiple daily injections (MDI) of insulin or continuous subcutaneous insulin injection (CSII) by insulin pumps. Another 26 million people in the United States suffer from type 2 diabetes, many of whom become insulin dependent. In total, nearly six million Americans depend on insulin.[2] For these patients, choosing the correct dose and type of insulin to take, and when to take it, remains a significant challenge. The goal of an insulin regimen is to maintain blood glucose concentration within a narrow range. Chronically high glucose levels, hyperglycemia, leads to severe health problems and premature death. Acute low glucose levels, hypoglycemia, can cause fainting, seizures, and death. Patients on MDI maintain a healthy glucose level by typically taking five to ten injections per day. These may include injection of a long-acting form of insulin before going to sleep and/or after waking, as well as short-acting insulin injections both before and after every meal and snack, with those following meals chosen to correct for prior insufficient doses. In order to make informed dosage decisions, most diabetics today monitor their blood glucose with small blood draws from a finger prick before each injection. An increasing number of diabetics also monitor their glucose using a continuous glucose monitor (CGM), which provides glucose readings with much higher frequency, commonly around once every five minutes. Although it has already been appreciated that this high frequency data can be used to alert users to problematic glucose levels as they arise, the wealth of data provided by these machines has, so far, been underutilized for the challenge of determining optimal insulin doses.
An “artificial pancreas” is a heavily researched future treatment device for diabetes that uses a closed loop between a CGM and an insulin pump. Given frequent, accurate readings from a CGM, the insulin pump should be able to determine, without human intervention, how much insulin to give. This will require the development of an algorithm that can precisely calculate the response of blood glucose to insulin.
Previous inventions in the field of glucose prediction have used the time-series of glucose over a narrow window in the recent past, possibly around 30 minutes, to predict glucose over a similar time frame in the future, without directly accounting for external factors like insulin, dietary carbohydrates, and exercise. The previous inventions also often focus on making universal predictions of insulin response that are not individualized to each patient and they predict a single, time-integrated response rather than response as a function of time.
U.S. Patent Publication No. 2011/0160555 to Reifman, published Jun. 30, 2011, for “Universal models for predicting glucose concentration in humans” “utilizes similarities in the short-term (30 minutes or less) dynamics of glucose regulation in different diabetic individuals to develop a single, universal autoregressive (AR) model for predicting future glucose levels across different patients.” This patent does not account for external factors that cause changes in glucose or attempt to learn individualized models for different patients. Similarly, U.S. Pat. No. 9,076,107, issued Jul. 7, 2015, to Cameron et al. for “Neural network for glucose therapy recommendation” uses recent glucose trends to predict future glucose trends, with the model trained on data from multiple patients rather than individualized. U.S. Pat. No. 7,695,434, issued Apr. 13, 2010, to Malecha for “Medical device for predicting a user's future glycemic state” uses CGM data to predict future glycemic state using a Hidden Markov Model and not linear regression. This method does not use information other than the time series of glucose before the moment of prediction to predict future glucose.
U.S. Pat. No. 7,404,796, issued Jul. 29, 2008, to Ginsberg for “System for determining insulin dose using carbohydrate to insulin ratio and insulin sensitivity factor” is a method for finding individualized carbohydrate-to-insulin ratios (CIRs) and insulin sensitivity factors (ISFs). At its greatest level of detail, that method gives the integrated effect of a unit of insulin or a carbohydrate on a user's glucose concentration, and not the effect as a function of time. That method also does not use linear regression.
U.S. Patent Publication No. 2014/0073892, published Mar. 13, 2014, to Jette Randloev et al., for “Glucose predictor based on regularization networks with adaptively chosen kernels and regularization parameters” describes a method for predicting glucose based on data sets that are sparsely sampled in time, which does not make use of the glucose time series data made available by CGMs and the insulin time series data made available by insulin pumps. That method uses regularization networks with adaptively chosen kernels and regularization parameters and not linear regression.